{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male   19            19000          0\n",
       "1    15810944    Male   35            20000          0\n",
       "2    15668575  Female   26            43000          0\n",
       "3    15603246  Female   27            57000          0\n",
       "4    15804002    Male   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863  Female   46            41000          1\n",
       "396  15706071    Male   51            23000          1\n",
       "397  15654296  Female   50            20000          1\n",
       "398  15755018    Male   36            33000          0\n",
       "399  15594041  Female   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(r'C:\\Users\\碌卡\\Desktop\\python编程学习\\python数据分析\\100daysML_file\\100day\\datasets\\Social_Network_Ads.csv')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:,[2,3]].values\n",
    "Y = dataset.iloc[:,4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,Y,test_size = 1/4,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier = SVC(kernel='linear',random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 1 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0 0\n",
      " 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1]\n",
      "0.88\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "score = classifier.score(X_test,y_test)\n",
    "print(y_pred)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  5]\n",
      " [ 7 25]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.90      0.93      0.91        68\n",
      "          1       0.83      0.78      0.81        32\n",
      "\n",
      "avg / total       0.88      0.88      0.88       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "X_set,Y_set = X_train,y_train\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_set,Y_set = X_test,y_test\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01)\n",
    "                   ,np.arange(start=X_set[:,1].min()-1,stop=X_set[:,1].max()+1,step=0.01))\n",
    "plt.contourf(X1,X2\n",
    "            ,classifier.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape)\n",
    "            ,alpha = 0.75\n",
    "            ,cmap = ListedColormap(('red','green')))\n",
    "plt.xlim(X1.min(),X1.max())\n",
    "plt.ylim(X2.min(),X2.max())\n",
    "for i,j in enumerate(np.unique(Y_set)):\n",
    "    plt.scatter(X_set[Y_set==j,0]\n",
    "               ,X_set[Y_set==j,1]\n",
    "               ,c = ListedColormap(('red','green'))(i)\n",
    "               ,label=j)\n",
    "plt.title('LOGISTIC(Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "34dfee18d5f4a96df9a8fcc719c91cf50e8ed50de2aa108bf45cd20982063274"
  },
  "kernelspec": {
   "display_name": "Python 3.7.0 64-bit ('base': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
